Log In Sign up
The largest database of trusted experimental protocols

Micasense rededge cameras

Manufactured by Agisoft
Visit Supplier

The MicaSense RedEdge cameras are multispectral imaging sensors designed for professional applications. These cameras capture images in multiple spectral bands, including red, green, blue, red-edge, and near-infrared wavelengths. The core function of the MicaSense RedEdge cameras is to provide accurate and reliable data for various remote sensing and precision agriculture applications.

Automatically generated - may contain errors

Lab products found in correlation

Metashape api, by Agisoft (4 mentions)

4 protocols using micasense rededge cameras

1

Generating Orthomosaics with Multispectral Imagery

Check if the same lab product or an alternative is used in the 5 most similar protocols
To ensure the reflectance quality of the orthomosaic, we followed the steps suggested by Agisoft and MicaSense RedEdge cameras (Agisoft, https://bit.ly/32swtn2). These steps include the usage of the MicaSense downwelling light sensor to fix any illumination issues caused by the weather conditions and MicaSense reflectance calibration panel. The acquired images were processed through Agisoft MetaShape Pro software (Version 1.2.2, Agisoft LLC, http://www.agisoft.com) and its Python API (Application Program Interface) generates and exports a five-band orthomosaic and digital elevation models (DEM) automatically as GeoTIFF format. Our processing workflow includes following nine main steps (1) Uploading UAV images, (2) calibration, (3) GCPs detection and geo-tagging, (4) photo alignment, (5) camera optimization (6) build dense point cloud, (7) build DEM, (8) build orthomosaic (9) export DEM and orthomosaic (Additional file 2: Figure S1). In step three, coded GCPs are automatically detected through Agisoft Metashape API (Fig. 1e).
Selvaraj M.G., Valderrama M., Guzman D., Valencia M., Ruiz H, & Acharjee A. (2020). Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz). Plant Methods, 16, 87.
+ Open protocol
+ Expand
2

Multispectral Orthomosaic Generation Workflow

Check if the same lab product or an alternative is used in the 5 most similar protocols
To ensure the re ectance quality of the orthomosaic, we followed the steps suggested by Agisoft and MicaSense RedEdge cameras (Agisoft, https://bit.ly/32swtn2). These steps include the usage of the MicaSense downwelling light sensor to x any illumination issues caused by the weather conditions and MicaSense re ectance calibration panel. The acquired images were processed through Agisoft MetaShape Pro software (Version 1.2.2, Agisoft LLC, http://www.agisoft.com) and its Python API (Application Program Interface) generates and exports a ve-band orthomosaic and digital elevation models (DEM) automatically as GeoTIFF format. Our processing work ow includes following nine main steps (1) Uploading UAV images, (2) calibration, (3) GCPs detection and geo-tagging, (4) photo alignment, (5) camera optimization (6) build dense point cloud, (7) build DEM, (8) build orthomosaic (9) export DEM and orthomosaic (Figure S1). In step three, coded GCPs are automatically detected through Agisoft Metashape API (Figure 1e).
Selvaraj M.G., Valderrama M., Cordero D., Valencia M.O., Ruiz H., & Acharjee A. (2020). Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).
+ Open protocol
+ Expand
3

Optimizing Orthomosaics from UAV Imagery

Check if the same lab product or an alternative is used in the 5 most similar protocols
To ensure the reflectance quality of the orthomosaic, we followed the steps suggested by Agisoft and MicaSense RedEdge cameras (Agisoft, https://bit.ly/32swtn2). These steps include the usage of the MicaSense downwelling light sensor to fix any illumination issues caused by the weather conditions and MicaSense reflectance calibration panel. The acquired images were processed through Agisoft MetaShape Pro software (Version 1.2.2, Agisoft LLC, http://www.agisoft.com) and its Python API (Application Program Interface) generates and exports a five-band orthomosaic and digital elevation models (DEM) automatically as GeoTIFF format. Our processing workflow includes following nine main steps (1) Uploading UAV images, (2) calibration, (3) GCPs detection and geo-tagging, (4) photo alignment, (5) camera optimization ( 6) build dense point cloud, (7) build DEM, (8) build orthomosaic ( 9) export DEM and orthomosaic (Figure S1). In step three, coded GCPs are automatically detected through Agisoft Metashape API (Figure 1e).
Selvaraj M.G., Valderrama M., Cordero D., Valencia M.O., Ruiz H., & Acharjee A. (2020). Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).
+ Open protocol
+ Expand
4

Multispectral Orthomosaic Generation Workflow

Check if the same lab product or an alternative is used in the 5 most similar protocols
To ensure the re ectance quality of the orthomosaic, we followed the steps suggested by Agisoft and MicaSense RedEdge cameras (Agisoft, https://bit.ly/32swtn2). These steps include the usage of the MicaSense downwelling light sensor to x any illumination issues caused by the weather conditions and MicaSense re ectance calibration panel. The acquired images were processed through Agisoft MetaShape Pro software (Version 1.2.2, Agisoft LLC, http://www.agisoft.com) and its Python API (Application Program Interface) generates and exports a ve-band orthomosaic and digital elevation models (DEM) automatically as GeoTIFF format. Our processing work ow includes following nine main steps (1) Uploading UAV images, (2) calibration, (3) GCPs detection and geo-tagging, (4) photo alignment, (5) camera optimization (6) build dense point cloud, (7) build DEM, (8) build orthomosaic (9) export DEM and orthomosaic (Figure S1). In step three, coded GCPs are automatically detected through Agisoft Metashape API (Figure 1e).
Selvaraj M.G., Valderrama M., Cordero D., Valencia M.O., Ruiz H., & Acharjee A. (2020). Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!